{
  "abstract": "Neutrino momentum reconstruction at hadron colliders is intrinsically ambiguous because the longitudinal momentum is not directly observed. We study this problem in semileptonic $t\\bar{t}$ events using \\monster{} (Mixture of Neutrino Solutions with Transformer Event Representation), a mixture density network that predicts a multivariate normal mixture for the conditional distribution of neutrino momentum from reconstructed event objects. The model uses a Transformer-based event encoder and yields a sampling-free point estimate of the neutrino momentum from the closed-form density. On the public benchmark introduced with \\nuflows, \\monster{} reduces the 68th percentile of the three-momentum residual by 5\\% and the 95th percentile by 6\\% relative to the empirical-mode \\nuflows{} baseline, with per-variable root-mean-square errors smaller by 2 to 9\\% at comparable bias, while running 2 to 8.5 times faster at inference, depending on the device and batch size. These results indicate that mixture density networks are a competitive alternative to normalizing flows for neutrino reconstruction.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.22979",
  "author_total": 3,
  "authors": [
    {
      "name": "Seungjin Yang"
    },
    {
      "name": "Jason S. H. Lee"
    },
    {
      "name": "Junghwan Goh"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/ervqbpamjwgjb.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.22979",
  "fulltext_url": null,
  "id": "ervqbpamjwgjb",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.22979",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "http://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "hep-ex"
    ]
  ],
  "openalex": null,
  "pdf_url": "https://arxiv.org/pdf/2609.22979",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/ervqbpamjwgjb.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:23:56Z",
      "native_id": "2609.22979",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.22979"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:hep-ex",
      "subfield": "high-energy"
    }
  ],
  "title": "Mixture Density Networks for Neutrino Reconstruction at Hadron Colliders",
  "title_text": "Mixture Density Networks for Neutrino Reconstruction at Hadron Colliders",
  "type": "preprint",
  "updated_at": "2026-10-30T12:23:56Z",
  "url": "https://research.draconitic.com/paper/ervqbpamjwgjb",
  "venue": "arXiv",
  "versions": [
    {
      "doi": "10.1103/lz58-x4s8",
      "pid": null,
      "relation": "published-as",
      "title": null,
      "venue": null
    }
  ]
}